mSimCC (version 0.0.3)

qalys: Aggregate data from a microsimulated cohort

Description

Aggregates data from a microsimulated cohort.

Usage

qalys(scenario, disc=FALSE)

Value

Global and per-person QALYs of the considered prevention strategy.

Arguments

scenario

microsimulated cohort.

disc

discount rate to be applied. Defaults to FALSE (undiscounted).

Author

David Moriña (Universitat de Barcelona), Pedro Puig (Universitat Autònoma de Barcelona) and Mireia Diaz (Institut Català d'Oncologia)

References

Georgalis L, de Sanjosé S, Esnaola M, Bosch F X, Diaz M. Present and future of cervical cancer prevention in Spain: a cost-effectiveness analysis. European Journal of Cancer Prevention 2016;25(5):430-439.

Moriña D, de Sanjosé S, Diaz M. Impact of model calibration on cost-effectiveness analysis of cervical cancer prevention 2017;7.

See Also

mSimCC-package, microsim, costs, le, plotCIN1Incidence, plotCIN2Incidence, plotCIN3Incidence, plotIncidence, plotMortality, plotPrevalence, bCohort, yls

Examples

data(probs)
nsim       <- 3
p.men      <- 0
size       <- 20
min.age    <- 10
max.age    <- 84

#### Natural history
hn <- microsim(seed=1234, nsim, probs, abs_states=c(10, 11), sympt_states=c(5, 6, 7, 8), 
               prob_sympt=c(0.11, 0.23, 0.66, 0.9), 
                size, p.men, min.age, max.age, 
                utilityCoefs = c(1, 1, 0.987, 0.87, 0.87, 0.76, 0.67, 0.67, 0.67, 0.938, 0, 0),
                costCoefs.md = c(0, 0, 254.1, 1495.9, 1495.9, 5546.8, 12426.4, 23123.4, 
                                 34016.6, 0, 0, 0),
                costCoefs.nmd = c(0, 0, 81.4, 194.1, 194.1, 219.1, 219.1, 219.1, 219.1, 0, 0, 0),
                costCoefs.i = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), disc=3, 
                treatProbs=c(0,0,1,1,1,0.9894,0.9422,0.8262,0.5507,0,0,0),
                nCores=1) ### individual level

qalys(hn)